Optimizing Maintenance Life for Transport Fleets in A Poultry Meat Supply Chain

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Arak University, Arak, Iran.

2 Industrial & Systems Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran

3 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Abstract

Modern maintenance scheduling is a complex optimization problem that combines resource constraints, uncertain environments, and critical times. In this research, the maintenance life of logistics tools within an agricultural product supply chain is optimized. Like any other supply chain, an agricultural supply chain is a network of organizations working together in various processes and activities to bring products and services to the market, aiming to meet customer demands. This study focuses on increasing the maintenance life of trucks and reducing transportation costs by optimizing the periodic repair time of trucks in the chicken meat distribution network, specifically from the slaughterhouse in Rasht to retailers in Tehran, Iran. The designed optimization problem was simulated and solved using a gradient-based method and the concept of nonlinear programming in the MATLAB software environment. By using this method, the simulation results indicate a 16% reduction in maintenance costs.

Keywords

References

Arora, V. K., et al. (2014). Fatigue life assessment of 65Si7 leaf springs: a comparative study. International scholarly research notices, 2014.
Barreto, S., et al. (2007). Using clustering analysis in a capacitated location-routing problem. European Journal of Operational Research, 179(3), 968-977.
Cavalcante, C. A., et al. (2018). A general inspection and opportunistic replacement policy for one-component systems of variable quality. European Journal of Operational Research, 266(3), 911-919.
Chaves, A. A., & Lorena, L. A. N. (2010). Clustering search algorithm for the capacitated centered clustering problem. Computers & Operations Research, 37(3), 552-558.
de Jonge, B., et al. (2015). Optimum maintenance strategy under uncertainty in the lifetime distribution. Reliability engineering & system safety, 133, 59-67.
De Jonge, B., & Scarf, P. A. (2020). A review on maintenance optimization. European Journal of Operational Research, 285(3), 805-824.
Ding, H., et al. (2009). Stochastic multi-objective production-distribution network design using simulation-based optimization. International Journal of Production Research, 47(2), 479-505.
Fouladirad, M., et al. (2018). Sensitivity of optimal replacement policies to lifetime parameter estimates. European Journal of Operational Research, 266(3), 963-975.
Fu, Y., et al. (2020). Optimum component reallocation and system replacement maintenance for a used system with increasing minimal repair cost. Reliability engineering & system safety, 204, 107137.
Hrušovský, M., et al. (2024). Integrated planning approach for fleet sizing and fleet management of freight railcars. Flexible Services and Manufacturing Journal, 1-35.
Hunt, I., et al. (2005). Applying the concepts of extended products and extended enterprises to support the activities of dynamic supply networks in the agri-food industry. Journal of food engineering, 70(3), 393-402.
Insua, D. R., et al. (2020). Advances in Bayesian decision making in reliability. European Journal of Operational Research, 282(1), 1-18.
Kansal, L., & Ediga, P. (2024). IoT-Enabled predictive maintenance for sustainable transportation fleets. MATEC Web of Conferences,
Kumar, M. S., & Vijayarangan, S. (2007). Static analysis and fatigue life prediction of steel and composite leaf spring for light passenger vehicles.
Lolli, F., et al. (2022). Age-based preventive maintenance with multiple printing options. International Journal of Production Economics, 243, 108339.
Musa, R., et al. (2010). Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers & Industrial Engineering, 59(1), 85-92.
Peña, D., et al. (2023). A novel multi-objective optimization approach to guarantee quality of service and energy efficiency in a heterogeneous bus fleet system. Engineering Optimization, 55(6), 981-997.
Sharma, R., et al. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926.
Taboada, H., et al. (2022). Mathematical Modeling and Optimal Control for a Class of Dynamic Supply Chain: A Systems Theory Approach. Applied Sciences, 12(11), 5347.
Tusar, M. I. H., & Sarker, B. R. (2022). Maintenance cost minimization models for offshore wind farms: A systematic and critical review. International Journal of Energy Research, 46(4), 3739-3765.
Ukato, A., et al. (2024). Optimizing maintenance logistics on offshore platforms with AI: Current strategies and future innovations. World Journal of Advanced Research and Reviews, 22(1), 1920-1929.
Zhao, X., et al. (2015). Which is better for replacement policies with continuous or discrete scheduled times? European Journal of Operational Research, 242(2), 477-486.
Journal of Optimization and Supply Chain Management
Volume 1, Issue 2
December 2024
Pages 133-146

Files

Share

How to cite

Statistics